data analyticsData is everywhere around us. A report shows that people, things, and organizations are generating 2.5 quintillion bytes of data each day.

It is a staggering figure indeed, but there is a clear explanation for it. For example, you are not only reading this post right now but also leaving digital traces about your content interests and website browsing habits. The same goes for all the other Internet users.

Although it can tell a lot about modern consumers, the truth is that businesses ignore almost 90% of user-related information. In other words, businesses are wasting the potential to understand their audiences and design better products, services, and marketing campaigns.

On the other hand, companies that harness big data’s full power could increase their operating margins by up to 60%. Data analytics makes it possible, but it takes a skilled team of analysts who understand the fundamentals of research methodologies. If you are interested to learn more about it, keep reading our article to see the top 10 types of data analysis methods. 

  • Statistical analysis

We open the list with one of the most common methods called the statistical analysis. You can use it for everything from small data sets to enormous information libraries with seemingly unrelated pieces of content.

Statistical analysis is the game of numbers, but researchers also define it as the science of collecting, exploring and presenting large amounts of data to discover underlying patterns and trends.

When we say you can use it everywhere, we really mean it. For example, a research paper writing service can use statistical analysis to explore custom papers and improve its assignment help services. Of course, there are thousands of other applications depending on a given business.

  • Descriptive analysis

Just like the name suggests, descriptive data analysis is supposed to answer the question: What really happened here? By definition, descriptive statistics is the term given to the analysis of data that helps describe, show or summarize data in a meaningful way such that, for example, patterns might emerge from the data.

The methodology is often used for KPI tracking. The goal is simple – you have a clear set of key performance indicators and descriptive statistics will show you the outcome based on the actual performance of the company.   

It’s a great method for periodical reports, sales overviews, and other types of basic analyses. 

  • Diagnostics analysis

If the descriptive analysis reveals the “what” side of the story, then diagnostics analysis will tell you why something really took place. The purpose is to dig deeper into the matter and discover the causes of a given concept, event, or phenomenon. 

Diagnostics helps companies to detect connections between business parameters. For instance, content writing agencies often rely on diagnostics analysis to learn why the top resume reviews or college paper reviews are better or worse than expected.

Online retail stores also exploit the strategy to figure out which marketing campaigns help them skyrocket conversions. Once again, the number of possible use cases is limitless. 

  • Narrative analysis

If you want to analyze the business through words rather than numbers, then narrative analysis might be the perfect solution for your organization. The idea behind narrative analysis is to research ideas, attitudes, opinions, and stories. 

That way, you can discover the major preferences among workers and their HR software and reconsider the entire organizational culture. But let’s focus on practical examples of narrative analysis. 

For instance, you can conduct research to learn employees’ attitudes or emotions about their current positions or relationships with their colleagues. You can use it to explore customers’ attitudes. Whenever there’s a need to analyze emotions, feelings, and opinions, narrative analysis is there to help you. 

  • Predictive analysis

Here’s another type of data analytics methodology that you can figure out by the name itself. We are talking about predictive analysis, a strategy mainly used for the identification of trends and future business outcomes. 

Instead of guessing or relying on your intuition for decision-making purposes, you can take advantage of predictive analysis to make promising data-driven decisions. It is basically a form of statistical modeling that makes logical conclusions on a given subject. 

What it really means in practice is that you can use predictive analysis to conduct risk assessments, forecast sales, pinpoint high-converting leads, and so on. 

  • Prescriptive analysis 

The main goal of business intelligence is to find ways to make better and more accurate decisions. This is exactly what makes prescriptive analysis so valuable, but also difficult. It’s a combination of descriptive and predictive analyses. 

By definition, prescriptive analytics is the area of business analytics dedicated to finding the best course of action for a given situation. The downside of this methodology is the logistics as prescriptive analytics usually requires a lot of manpower and substantial budgeting. 

  • Content analysis

If you are analyzing qualitative data, you might as well give the content analysis a try. Content analysis is a research tool used to determine the presence of certain words, themes, or concepts within some given qualitative data. 

The strategy is commonly used in discourse and textual analyses, which is why it’s so popular among custom papers and assignment help services. Content analysis is a great weapon of choice for companies eager to understand their consumers because it helps decision-makers to figure out the true meaning of reviews, interviews, surveys, and other sorts of feedback. 

  • Monte Carlo simulation

Businesses interested in risk mitigation should definitely rely on Monte Carlo simulations. The underlying concept is to use randomness to solve problems that might be deterministic in principle.  

If it sounds simple, that’s because it is. You won’t see business managers using the Monte Carlo method too frequently, but it is a precious data analytics model if other methodologies don’t suit your current business needs.  

  • Text analytics

Text analytics is the automated process of translating large volumes of unstructured text into quantitative data to uncover insights, trends, and patterns. People also call it text mining. The methodology often works in combination with data visualization platforms, thus resulting in a more meaningful overview of the targeted process or phenomenon. 

As the Internet is packed with all sorts of textual posts, a company may use text analytics to understand and evaluate brand-related content. This helps a lot of companies to keep their online reputation flawless despite dealing with thousands or even millions of customers worldwide. 

  • Hypothesis testing

The last methodology on our list is hypothesis testing, also known as T Testing. Using this technique, you are trying to prove or disprove the results of your research. In other words, you are basically testing whether your results are valid by figuring out the odds that your results have happened by chance.

This is the best methodology to use if you are trying to prove the correlation between certain aspects of the business. For example, you can claim that less time spent on social media leads to higher employee engagement. But before you actually limit the social media usage at work, you need to test it and prove your point.

The Bottom Line

In a world where people and organizations leave all sorts of digital footprints on the Internet, data analytics makes all the difference between top-level businesses and under performers. It is necessary for every company to understand the needs of the target audience, which is exactly why data research plays such a major role in modern business. In this post, we discussed the top 10 types of data analysis methods. Don’t hesitate to use these models and improve the functioning of your organization.